GeeCon 2011 - NoSQL and In Memory Data Grids from a developer perspective

NoSQL & DataGrids from a Developer Perspective

Cyrille Le Clerc - Michaël Figuière

Speaker

Cyrille Le Clerc

@cyrilleleclerc

blog.xebia.fr

Apache CXF DataGrids

Large Scale

Speaker

Michaël Figuière

@mfiguiere

blog.xebia.fr

Search Engines NoSQL

DistributedSystems

About NoSQL

No SQL

About NoSQL

No SQL

Not Only

About NoSQL

No SQLRelational

Not Only

Once upon a time...

On the Web side

• Huge amount of data

• High availability

• Fault tolerance

• Scalability on commodity hardware

Similar needs for Web giants :- Created Dynamo- < 40 min of unavailability per year

- Created BigTable & MapReduce- Stores every webpages of Internet

Amazon : the birth of Dynamo

Fill cart Checkout Payment Process order Prepare Send

Requires high availability,key-value store is enough

Requires complex requests,temporal unavailability is acceptable

On the Financial side

• Very low latency

• Rich queries & transactions

• Scalability

• Data consistency

Needs within financial market :- Released Coherence in 2001- Started as a distributed cache

- Released Gigaspaces XAP in 2001- Routes the request inside the data

Data Partitioning and Replication

Use Case : Train Ticketing System

With trains, stations, seats, booking and passengers

Store everything in a Mainframe !

Up to 3 To of RAM ! More than $1,000,000

IBM z11

Data Partitioning

Split data for scalability

MainFrame

Smallservers

Partition gamma

Partition beta

Partition alpha

Data Replication

synchro

Duplicate data for high availability and

scalability

Partition alpha

Node 1

Node 2

Node 3

Partitioned Data Modeling

Partitioned Data Modeling

TrainStopdate

TrainStationcodename

Traincodetype

Seatnumberprice

Bookingreduction

Passengername

Typical relational data model

Partitionned Data Modeling

TrainStopdate

Seatnumberprice

Bookingreduction

Passengername

Reference data

Duplicated in each partition


Root entity

Partitioning ready entities tree

Traincodetype

Find the root entity and denormalize


Remove unused data

TrainStopdate

Seatnumberprice

Bookingreduction

Passengername

booked


Traincodetype


TrainStopdate


Seatnumberpricebooked

Traincodetype

Sharding ready data structure

Consistency, Availability and Partition Tolerance

Data Consistency with replicas

Node 1

write to all

read from one

{ "name": "Barbie Computer", "price": 15.50, "tags" : [ "doll", "barbie" ]}

Node 2

Node 3

Node 1

Node 2

Node 3



write to one

read from all

Node 1

Node 2

Node 3

Node 1

Node 2

Node 3


• You can adjust the balance between number of writes and number of reads

• See Eventual Consistency

Data Consistency with Multiple Data Centers

West Coast

East Coast




{ "name": "Barbie Computer", "price": 20.00, "tags" : [ "doll", "barbie" ]} { "name": "Barbie Computer",

"price": 15.50, "tags" : [ "doll", "barbie" ]}

set price to $ 20.00

propagation delay !

West Coast

East Coast



"price": 15.50, "tags" : [ "doll", "barbie", “girl” ]}


add tag “girl”reconciliation API needed !

West Coast

East Coast



"price": 15.50, "tags" : [ "doll", "barbie", “girl” ]}


add tag “girl”Network partitioning

West Coast

East Coast


TokyoNew York

London

World wide replicationfor financial market

CAP Theorem

Consistency

Availability

PartitionTolerance

Only 2 of these 3 properties can be

achieved in storage system

CAP Theorem

Impossible

Relational DBNoSQL DB Consistency

Availability

PartitionTolerance

Data models & APIs

Request Driven Data Modeling

• Relational data modeling is business driven

• With partitioning, data modeling had to be adapted for requests

• NoSQL & DataGrids data modeling is request driven

Adaptation to requests comes with tuning

Because network latency matters

Two requests may require to store data twice

Key-Value Store

In memory

Persistent

In memory with async persistence

Example with a user profile

johndoe User profile as byte[]

Similar to a Java HashMap

Write Example with Riak

RiakClient riak = new RiakClient("http://server1:8098/riak");

RiakObject userProfileObj = new RiakObject("bucket", "johndoe", serializer.serialize(userProfile);

riak.store(userProfileObj);

Inserts a user profile into Riak

http://localhost:8098/riak

http://localhost:8098/riak

Read Example with Riak

FetchResponse response = riak.fetch("bucket", "johndoe");

if (response.hasObject()) {

userProfileObj = response.getObject();

}

Fetch a user profile using its key in Riak

Column Families Store


Relational DB Column families DB

For each Row ID we have a list of key-value pairs

Key-value pairs are

sorted by keys

Example with a shopping cart

17:21 Iphone 17:32 DVD Player 17:44 MacBookjohndoe

6:10 Camera 8:29 Ipadwillsmith

14:45 PlayStation 15:01 Asus EEE 15:03 Iphonepitdavis

Write Example with Cassandra

Cluster cluster = HFactory.getOrCreateCluster("cluster", new CassandraHostConfigurator("server1:9160"));

Keyspace keyspace = HFactory.createKeyspace("EcommerceKeyspace", cluster);

Mutator<String> mutator = HFactory.createMutator(keyspace, stringSerializer);

mutator.insert("johndoe", "ShoppingCartColumnFamily", HFactory.createStringColumn("14:21", "Iphone"));

Inserts a column into the ShoppingCartColumnFamily

Read Example with Cassandra

SliceQuery<String, String, String> query = HFactory.createSliceQuery(keyspace, stringSerializer, stringSerializer, stringSerializer);

query.setColumnFamily("ShoppingCartColumnFamily") .setKey("johndoe") .setRange("", "", false, 10);

QueryResult<ColumnSlice<String, String>> result = query.execute();

Reads a slice of 10 columns from ShoppingCartColumnFamily

Document Store

Example with an item of a catalog

{ "name": "Iphone", "price": 559.0, "vendor": "Apple", "rating": 4.6, "tags": [ "phone", "touch" ]}

item_1

The database is aware of document’s fields and

can offers complex queries

Write Example with MongoDB

Mongo mongo = new Mongo("mongos_1", 27017);DB db = mongo.getDB("Ecommerce");DBCollection catalog = db.getCollection("Catalog");

BasicDBObject doc = new BasicDBObject();doc.put("name", "Iphone");doc.put("price", 559.0);

catalog.insert(doc);

Inserts an item document into MongoDB

Read Example with MongoDB

BasicDBObject query = new BasicDBObject();query.put("price", new BasicDBObject("$lt", 600));DBCursor cursor = catalog.find(query);

while(cursor.hasNext()) { System.out.println(cursor.next());}

Queries for all items with a price lower than 600

In Memory Data Grids

eXtreme Scale

Example with train booking with IBM eXtremeScale

With Data Grids,sub entities can have cross relations

@Entity(schemaRoot=true)public class Train { @Id String code; @Index @Basic String name; @OneToMany(cascade=CascadeType.ALL) List<Seat> seats = new ArrayList<Seat>(); @Version int version;

...}

TrainStopdate

Seatnumberpricebooked

Traincodetype

Write Example with IBM eXtreme Scale

void persist(Train train) { entityManager.persist(train);}

Inserts a train into eXtreme Scale

eXtreme Scale provides a JPA Style API

Read Example with IBM eXtreme Scale

/** Find by key */Train findById(String id) { return (Train) entityManager.find(Train.class, id);}

/** Query Language */Train findByTrain(String code) { Query q = entityManager.createQuery("select t from Train t where t.code=:code"); q.setParameter("code", code);

return (Train) q.getSingleResult();}

Simple and complex queries with eXtreme Scale

More APIs

• Another Java EE versus Spring battle ? JSR 347 Data Grids vs. Spring Data

Unified API ontop of relational, document, column, key-value ?

Object to tuple projection API

Transactions

Transactions

• NoSQL usually means NO transactions

• Except when it means eXtreme Transactions !

Transactions Concurrency

warehouse stocks

231

264

2

637

canon-eos: 1ipod : 1headphone : 1iphone: 1...

ipad : 1 iphone: 1

barbie : 1iphone: 1cabbage-doll: 1

concurrency on iphone

121

311

Place order

cancel order if one product is missing

12

SQL Transactions

warehouse stocks

231

264

2

637


lock duration = f(shoppingcart.length)if too many locks on the rows, then lock table !

beginfor each shoppingCart.product select for update ... update ...commit

121

311

12

ipad : 1 iphone: 1


Place order

SQL Transactions

warehouse stocks

231

264

2

637



select for update ...

121

311

12

ipad : 1 iphone: 1


Place order

SQL Transactions

warehouse stocks

231

264

2

637



select for update ...

121

311

12

ipad : 1 iphone: 1


Place order

Transactions with Manual Compensation

warehouse stocks

231

264

2

637

code “do”, “undo” and the chain

121

311

12

if(stock - quantity > 0) { stock = stock - quantity;} else { throw exception() !

DO

stock = stock + quantity;

UNDO

-1

-1if(stock - quantity > 0) { stock = stock - quantity;} else { throw exception() !

DO


UNDO


DO


UNDO


DO


UNDO

-1

-1if(stock - quantity > 0) { stock = stock - quantity;} else { throw exception() !}

DO


UNDO


Place order


warehouse stocks

231

264

0

637

121

311

12


DO


UNDO

-1


DO


UNDO


DO


UNDO

-1


Place order


warehouse stocks

231

264

0

636

121

311

12


DO


UNDO

-1


DO


UNDO


DO


UNDO

-1


Place order


warehouse stocks

231

264

0

636

121

311

12


DO


UNDO

-1


DO


UNDO


DO


UNDO

-1

no more iphone !barbie : 1iphone: 1cabbage-doll: 1

Place order


warehouse stocks

231

264

0

636

121

311

12


DO


UNDO

-1


DO


UNDO


DO


UNDO

-1

interruptedbarbie : 1iphone: 1cabbage-doll: 1

cancelled

Place order


warehouse stocks

231

264

0

636 +1

121

311

12


DO


UNDO

-1

-1

-1

interruptedbarbie : 1iphone: 1cabbage-doll: 1

cancelledif(stock - quantity > 0) { stock = stock - quantity;} else { throw exception() !

DO


UNDO


DO


UNDO

undo

if(stock - quantity > 0) { stock = stock - quantity;} else { throw exception() !}

DO


UNDO

Place order


• Code “do” & “undo” & chain execution

• What about interrupted chain execution ? Data corruption ?


• Code “do” & “undo” & chain execution

• What about interrupted chain execution ? Data corruption ?

data store managed transaction chain execution

Which solution to choose?

Key-Value Store

• Get and Set by key

• Riak and Voldemort provide a great scalability

• Memcached and Redis offer low overhead and latency

Simple but enough for a lot of use cases

Great to persist continuously growing datasets

Great for cache and live data


• Get and Set by key of a list of columns

• Queries are simples, but columns slice fetching is possible

• Data model is too low level for many complex data modeling

Makes it possible to fetch and update partial data

Great for pagination

Should typically be used for the largest scalability needs

Document Store

• Schema less

• Complex queries are available

• Scalability may be limited if not querying using partition key

Great for continuously updated schemas

Necessary for filtering and search

Can be handle using multiple storage and limited queries

In Memory Data Grid

• Very Low Latency & eXtreme Transaction Processing (XTP)

• In Memory - No Persistence

• High budget and Developer skills required

Investment banking, booking & inventory systems

Most of the time backed with a database

Some Open Source alternatives are appearing

Polyglot storage for eCommerce

Application

Solr

MongoDB

Cassandra

Coherence

Productssearch

Warehouseinventory

Product catalog

User account and Shopping cart

Why NoSQL & DataGrids matter ?

• Polyglot Storage: databases that fit the needs of every type of data

• Linear Scalability: being able to handle any further business requirements

• High Availability: multi-servers and multi-datacenters

• Elasticity: natural integration with Cloud Computing philosophy

• Some new use cases now available

Questions / Answers

?

Technology

GeeCon 2011 - NoSQL and In Memory Data Grids from a developer perspective